Austin Community College · Higher Education · Austin, TX · 14 weeks
Student success analytics deployed to 220 advisors
Austin Community College relied on a manual process to identify at-risk students, which took three weeks each semester. This delay prevented timely intervention. We developed a student success analytics platform that flagged at-risk students within 48 hours of early warning signs, giving advisors enough time to act before students dropped out.
Challenge
The advising team spent three weeks each semester manually gathering enrollment, grade, and attendance data from three different systems and compiling it in Excel. This delayed identifying at-risk students, leaving just two to three weeks for interventions before the withdrawal deadline. As a result, the college lost between 1,800 and 2,200 students each semester to avoidable withdrawals.
Outcome
We cut the time to identify at-risk students from three weeks to two days. In the first semester using the new system, advisors reached out to 1,240 students who were likely to drop out based on past data. This led to a 4.2-point increase in retention, securing $3.1 million in tuition that otherwise would have been lost.
Results
- 1,240 Students retained in first semester
- 3 wks to 48 hrs At-risk identification time
- $3.1M Retained tuition revenue
- 220 Advisors using the platform
Before, we only identified at-risk students about three weeks into the semester, so we didn’t have much time to intervene. Now, we catch them within 48 hours. As a result, we prevented 1,240 students from dropping out in the first semester. Those are 1,240 students who stayed enrolled.